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دسته بندی:
بانکداری - Banking
سال انتشار:
2019
عنوان انگلیسی مقاله:
Predicting failure in the U:S: banking sector: An extreme gradient boosting approach
ترجمه فارسی عنوان مقاله:
پیش بینی شکست در بخش بانکی ایالات متحده: رویکرد افزایش شدید شیب
منبع:
Sciencedirect - Elsevier - International Review of Economics and Finance, 61 (2019) 304-323: doi:10:1016/j:iref:2018:03:008
نویسنده:
Pedro Carmona a, Francisco Climent b,*, Alexandre Momparler c
چکیده انگلیسی:
Banks play a central role in developed economies. Consequently, systemic banking crises destabilize
financial markets and hamper global economic growth. In this study, extreme gradient
boosting was used to predict bank failure in the U.S. banking sector. Key variables were identified
to anticipate and prevent bank defaults. The data, which spanned the period 2001 to 2015,
consisted of annual series of 30 financial ratios for 156 U.S. national commercial banks. Identifying
leading indicators of bank failure is vital to help regulators and bank managers act swiftly
before distressed financial institutions reach the point of no return. The findings indicate that
lower values for retained earnings to average equity, pretax return on assets, and total risk-based
capital ratio are associated with a higher risk of bank failure. In addition, an exceedingly high
yield on earning assets increases the chance of bank financial distress.
Keywords: Bank failure prediction | Bank failure prevention | Bank financial distress | Machine learning | Extreme gradient boosting | XGBoost
قیمت: رایگان
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